Data measurement using measurement probes and tools has been a common practice since the advent of measuring tools. The earliest tools, such as sun dials, wind vanes, sextants, and the like, were manually or naturally driven with the data simply written down or remembered. Newer probes include temperature sensors, weather probes, radio frequency (RF) sensors, global positioning system (GPS) receivers, and the like, are now driven by computers and electronics. The modern trend has evolved to using wireless probes for certain types of measurement tasks. Generally, a measurement task that may be targeted in stationary, remote locations or events that may be tracked across a wide area are all good candidates to use wireless measurement probes. For example, probes measuring wireless communication networks, traffic patterns, pollution levels, environmental conditions, and the like, are each have use for a remote probe that sends its measurements over the airwaves. This process generally relieves the cost to place human resources in the field and also allows for probes to be placed in extreme areas that may not typically be accessible or inviting to humans. By using a wireless probe, there is no need to run cabling to the remote location, which both relieves the costs involved for the cabling, but also may diminish the impact on the environment.
One of the problems with wireless probes, however, is the data bandwidth limitations of the wireless networks. Most measurement probes are capable of taking measurements at a rate well in excess of the rate at which the measured data can be transmitted over the wireless network. This data throughput mismatch creates a problem in getting the measured information to the processing point. Either data will have to be dropped or will have to be saved. Current solutions for mobile-type wireless probes generally involve the probe attached to a large storage facility, such as a large hard disk, or other type of storage. While this allows for a large amount of data to be measured and used in analysis, the measured data needs to be downloaded from the storage at the processing center before any processing may be done. Other solutions have involved the use of “smart” probes, which are probes that have a limited amount of embedded processing functionality. These smart probes may be programmed to control the actual measure-taking in some limiting, yet logical, fashion.
Such smart probes may be used to control the measurement process in bandwidth-sensitive ways. For example, if a phenomenon to be measured is really only interesting for a certain period of time, the probe may be programmed to make its measurements only during the times of interest. Similarly, if the phenomenon were only present in certain locations, the probe may be programmed to make measurements only when it is in those zones or locations of interest. Moreover, there may be phenomena that are interesting over a combination of time and location. In these cases, the probe may be programmed to measure only in the interesting times and locations. By strategically limiting the measurement process, the amount of raw data collected may be greatly reduced. However, while these measurement strategies greatly reduce the amount of raw measurement data is collected, the amount of data that may be collected by a probe within the limited zones of interest may still overwhelm any available bandwidth resources.